Adaptive low powered wide area communications

Can we make Low Powered Wide Area (LPWA) communications systems adaptive to ensure delivery of data according to application demands in dense city environments?

Partners

Imperial College London

In the theoretical study we are the first to derive an optimal value of active LoRa nodes per each cluster that maximizes the area spectrum efficiency and energy efficiency network wide and which takes interference from other LoRa and LPWA networks into account.

The LoRADAP simulation results have shown that the channel assignment design can achieve more than 80% performance improvements compared to a baseline method, but with the added advantage of much lower complexity, which extends the battery lifetime of the sensor devices.

In wireless networks that support LPWA communications (e.g. LoRa, Zigbee 868, SigFox etc), an important question that arises is how should a node select the best radio parameters taking into consideration current dynamical environmental conditions and varying application demands?

Our adaptive communications research has focused on the LoRa communications protocol, in order to optimize the way in which sensors communicate to a base station, therefore allowing for more nodes to transmit data reliably at the same time. This can be achieved by improving the process of selection of three parameters: the channel, spreading factor and transmission power.

Low-Power Wide-Area (LoRa) is a type of wireless network that enables long range communication at a low bit rate between sensor nodes and a base station. LoRa uses the 868 MHz band and comes with a protocol called LoRa WAN, which is a set of algorithms that defines three key aspects of the communication process: which channel the sensor node will send data on, at what spreading factor and finally, the amount of transmission power to be used for the communication. The main constraint of LoRa is that it operates at a maximum of 1% duty cycle.

We initially analysed the theoretical performance of LoRa to give us insight into how it should behave with other coexisting radio communications systems operating in the same environment; a situation important in dense city areas. More specifically, we considered a node topology of acluster-based city-wide hierarchy and built two models of network behaviour: one with a fixed number of active LoRa nodes and the other with a random number of active LoRa nodes. Within each model, two cases are explored: the first is LoRa node is selected either randomly or by the order of distance to the typical receiver.

Using stochastic geometry, we were able to determine theoretically the performance of these networks in terms of coverage probability, area spectrum efficiency, and energy efficiency. Our models have been verified by simulation and show that these models allow us to better place nodes in a cluster to maximise performance.

Using this knowledge we developed a new resource management protocol for LoRa with adaptive configurations, named LoRADAP, in order to avoid the spreading factor conflict as well as optimize transmit power.

LoRaDAP attempts to optimize the transmission by creating a preference list of channels for each node. This preference list is based on the counter each node has that keeps track of the time already spent in each channel. The LoRaDAP protocol transmits the available time per channel in the preferred order. This means that if a node has not transmitted a lot on one channel, this channel is more preferable because it has more available time to transmit data.

Subsequently, the server will check if there is a spreading factor conflict within each channel based on the proposals from nodes and reconfigure the conflicted nodes to avoid retransmission as much as possible. The server will then optimize the transmit power of nodes assigned into the same channel with the purpose of maximizing the achievable minimal signal to noise ratio; that is, maximising the achievable reliability of the communication. Finally, the server will send the updated configurations to each node for the upcoming transmission and thus the network’s performance is maximised dynamically.